To be data-driven, organizations and people must be data literate. Several characteristics contribute to data literacy
The world of data and analytics is inundated with catchy phrases and words: big data, AI, machine learning, even data literacy and data-driven. What is one to make of all of these? Are they real, do they have power behind them? Each of them is real and each of them has power, but people need to truly understand the topics to free themselves and empower themselves to be smart and use data effectively.
It is said that by 2025, the global datasphere (the sum of the world’s data) will reach 175 ZB, that is zettabytes. How many zeroes are in a zettabyte? 21! That is an immense amount of data and a lot of zeroes. That figure looks like this: 175,000,000,000,000,000,000,000 – a lot of information and data. Do organizations really know what to do with that data? Are organizations investing in tools and technology without a data strategy in place? Do organizations even know what a true data strategy is? A data strategy connects the data management and analytics processes of the organization directly to the organization’s business strategy.
The purpose of data is to empower an organization to make better decisions, that’s it. So, what is the definition of data-driven? Being data-driven means that an organization is using data to improve operations and make better decisions, helping to realize an organization’s strategy and business goals. What are key pillars for a data-driven organization?
Key Pillars of a Data-Driven Organization
During the pandemic, organizations realized the power of data can help an organization succeed during a dark time – and through good times as well. There are 5 pillars to a data-driven organization:
- Leadership
- Culture
- Tools and Technology
- Data Literacy
- Holistic data strategy
These characteristics are not listed in a particular order, but they form the basis of a data-driven organization. Leadership must actively support the use of data for decisions and must commit to managing data as an enterprise – not department – asset. Culture is the number one roadblock to data and analytic success, so the right environment must exist for an organization to use data effectively. Organizations must have the right tools and technology to support successful data usage, but organizations must not focus their data strategy on the tools and technology. The data strategy should help an organization pick the tools and technology. Data literacy is a major – and complex – component of any data-driven organization. Finally, the organization needs a holistic data strategy, which is a data strategy that ties to the business objectives. Each pillar matters, but organizations must address the data and analytics skills gap that exists almost everywhere.
Data Literacy Skills Gap
For data and analytics strategies to succeed, the data-related skills gap must be closed; that is the job of data literacy. People and organizations shouldn’t worry about the nuances of the terminology, but instead, they should learn the definitions, purposes, and context of the terms. Some argue that the term data-driven shouldn’t be used; they prefer “data informed”. Some may say that the term “data literate” can turn people off to data literacy because it can imply that people are “data illiterate”. The reality is, getting caught up with the nuances of terminology in the data and analytics world can waste precious time needed to drive data and analytical work forward.
It is important to stress that people use data all the time. Do people use data to read a gas gauge and understand pricing? When buying a house, do people study mortgage rates and payments? Everyone uses data in many ways, showing that people are more data literate than they know, and they have more skills with data than they realize. Are employees properly armed with data literacy skills? Being data literate means they truly know how to read, work with, analyze, and communicate with data.
Definition of Data Literacy – Data Literacy Characteristics
The definition of data literacy , as used at Qlik, is the ability to read, work with, analyze, and communicate with data. In this definition there are four main characteristics for data literacy. The most important of these characteristics is reading data. If one thinks about it, how can one work with data, analyze it, or communicate with data? Start by reading it! Reading data means to look at it and understand what it represents. That definition comes from the definition of “read”: look at and comprehend the meaning of written or printed matter by mentally interpreting the characters or symbols of which it is composed. Everyone should improve their ability to look at and read data, so they can understand it, and doing that without fear.
The second characteristic is the ability to work with data – to use it for making decisions. Now, this capability will vary from person to person, and can depend on one’s role. If one is a data scientist, they should be able to work with data more thoroughly than someone who does not work as a data analytics professional. If one is a C-suite member, they may not have the time to dig deeply into data, so they may just need to read it and work with (use) the data that is shared and presented to them. There is a wide array of skills within “working with data”.
The third characteristic of data literacy is the ability to analyze data. The four levels of analytics are descriptive, diagnostic, predictive, and prescriptive. Not everyone needs to be well versed within each level. In fact, with data literacy, most people will work within the first two levels of analytics: descriptive and diagnostic. The last two levels of analytics are more technical: predictive and prescriptive. It is important to have all four levels of analytics within an organization’s holistic data strategy, accepting that most organization will need analytics that fall into one of the first two categories.
To support analyzing data, one may just need to ask good questions. How? By knowing what to communicate to the data professionals – what are the issues or challenges that data may be able to solve – and have the data professionals dig in and answer those questions or solve those challenges and present those results – helping to improve data-based decisions.
The final characteristic of data literacy is to communicate with data. Think of this as the secret sauce of data and analytics. Imagine discovering data that supports an amazing bit of insight, but then one can’t communicate it satisfactorily. Would that be effective? Of course not! The world of data and analytics is full of terminology that can be difficult to understand. So, people should simplify and communicate about potentially complex things in uncomplicated language and do so throughout the organization. This suggestion applies to both the data professional and non-data professional.
How to Improve Your Data Literacy
Think of the 3 Cs of data literacy: be curious, be creative, think critically.
People should be more curious; ask more questions and discover why the data looks like it does. Ask if there a better way to display data to improve understanding. Are there different ways to view the data? Ask more questions of data. Be prepared to explore how to use data to address the situation.
Next, data and analytics can be seen as boring or intimidating. One should use one’s personal creativity with data to examine it, to use it, and to present it. Understand how a bit of creativity with language or a different perspective could help with viewing or interpreting the data.
Finally, people need to think critically about many things. No more just taking things at face value. Is this data all there is? Does it come from a trusted source? Is it presented objectively? What questions does this data raise? Why? Let the answers to these questions lead to confident use of data – or a discovery that different data is needed.
These 3 Cs are just the start of data literacy skills. Often, some technical skills or data literacy training may be needed, but it does not mean a person must become a data scientist to use data effectively. Being data-driven and data literate includes the human element; people possess wonderful skills that can be applied to data – and they need to use them! Sometimes the data will need to override the human element, and sometimes the human element will need to override the data. Overall, the key is to balance it out.
Conclusion
Not everyone needs to be a data scientist, but everyone needs to be data literate – and organizations need data literate people so they can become data-driven enterprises. People have many skills, but they need to develop them in ways that support using data confidently and effectively. Data literacy can become an amazing tool in one’s life and career, helping people and organizations thrive.
